Comparison between the ages for the types (after controlling for gender)

Questions

  • What are the differences between the ages for the different types
  • Do we observe the same changes as globally?

Age effect - General Questions

  • What are the differences between the ages?
  • Which genes and pathways are differentially expressed between 8w and 52w, between 52w and 104w, between 8w and 104w? Are they the same? Is there a gradient?
  • Are they different for the two genders?
  • Are they different for the two types?

Loads

Libraries and functions

Warning message in is.na(x[[i]]):
“is.na() applied to non-(list or vector) of type 'environment'”Warning message in rsqlite_fetch(res@ptr, n = n):
“Don't need to call dbFetch() for statements, only for queries”
==========================================================================
*
*  Package WGCNA 1.63 loaded.
*
*    Important note: It appears that your system supports multi-threading,
*    but it is not enabled within WGCNA in R. 
*    To allow multi-threading within WGCNA with all available cores, use 
*
*          allowWGCNAThreads()
*
*    within R. Use disableWGCNAThreads() to disable threading if necessary.
*    Alternatively, set the following environment variable on your system:
*
*          ALLOW_WGCNA_THREADS=<number_of_processors>
*
*    for example 
*
*          ALLOW_WGCNA_THREADS=4
*
*    To set the environment variable in linux bash shell, type 
*
*           export ALLOW_WGCNA_THREADS=4
*
*     before running R. Other operating systems or shells will
*     have a similar command to achieve the same aim.
*
==========================================================================


Allowing multi-threading with up to 4 threads.
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."
[1] "preparing gene to GO mapping data..."
[1] "preparing IC data..."

Data

Stats

Wald padj < 0.05LFC > 0 (Wald padj < 0.05)LFC < 0 (Wald padj < 0.05)
52w VS 8w (SPF)1391 796 595
52w VS 8w (GF) 600 367 233
104w VS 52w (SPF)1474 885 589
104w VS 52w (GF)1623 846 777
104w VS 8w (SPF)323117741457
104w VS 8w (GF)1861 972 889

Differentially expressed genes

  52w VS 8w (SPF)    52w VS 8w (GF) 104w VS 52w (SPF)  104w VS 52w (GF) 
        0.4335011         0.5316667         0.2890095         0.5009242 
 104w VS 8w (SPF)   104w VS 8w (GF) 
        0.4237078         0.5937668 
Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in pcls(G):
“initial point very close to some inequality constraints”Warning message in stack.default(getgo(rownames(l$deg), "mm10", "geneSymbol")):
“non-vector elements will be ignored”Warning message in stack.default(getgo(rownames(as.data.frame(l$deg)), "mm10", "geneSymbol", :
“non-vector elements will be ignored”

Counts

52w != 8w or 104w != 8w or 104w != 52w for SPF with abs(FC) > 2

52w != 8w or 104w != 8w or 104w != 52w for GF with abs(FC) > 2

Comparison of the numbers per types

Differentially expressed genes

Differentially more expressed genes

Differentially less expressed genes

DEG into gene co-expression network

  • White: up-regulated
  • Black: down-regulated
52w VS 8w 104w VS 52w 104w VS 8w
SPF
GF

GO analysis

Biological process

Dot-plot with the most over-represented BP GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Network based on description similarity

52w VS 8w 104w VS 52w 104w VS 8w
SPF
GF

52w VS 8w (SPF)

<!DOCTYPE html>

52w VS 8w (GF)

<!DOCTYPE html>

104w VS 52w (SPF)

<!DOCTYPE html>

104w VS 52w (GF)

<!DOCTYPE html>

Cellular components

Dot-plot with the most over-represented CC GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

Molecular functions

Dot-plot with the most over-represented MF GO (20 most significant p-values for the different comparison)

Using term, id as id variables
Using term, id as id variables

KEGG pathways

[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
Warning message in file.rename(from = paste("mmu", cat, ".pathview.multi.png", sep = ""), :
“cannot rename file 'mmu00533.pathview.multi.png' to '../results/dge/age-effect/age_type/kegg/over_repr_kegg/mmu00533.pathview.multi.png', reason 'No such file or directory'”
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."
[1] "Note: 7 of 5406 unique input IDs unmapped."

SPF effect in aging

Question: Is there any genes that shows an increasing fold change in SPF with aging while not for GF?

52w vs 8w for SPF 52w vs 8w for GF 104w vs 52w for SPF 104w vs 52w for GF Gene number
Set 1 != != 244
Set 2 != == != == 92
52w VS 8w (SPF)52w VS 8w (GF)104w VS 52w (SPF)104w VS 52w (GF)104w VS 8w (SPF)104w VS 8w (GF)
0610007P14RikNA NA NA 0.4582787 NA0.3508672
0610009B22RikNA NA NA 0.3173098 NA0.3199247
0610009L18RikNA NA NA NA-0.7734166 NA
0610009O20RikNA NA -0.7088350-0.4522004-0.4394075 NA
0610010F05RikNA NA 0.5226107 NA NA NA
0610010K14RikNA NA NA NA-0.3647340 NA
Gene number
Set 1244
Set 2 92

Genes with differential expression with aging in SPF but not in GF (set2)

52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) > 0

[1] 33

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = 23.489, df = 31, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9456141 0.9867280
sample estimates:
      cor 
0.9730381 

52w VS 8w (SPF) > 0 and 104w VS 52w (SPF) < 0

[1] 23

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = -19.401, df = 21, p-value = 6.881e-15
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9887658 -0.9368473
sample estimates:
       cor 
-0.9732205 

52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) > 0

[1] 24

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = -8.1923, df = 22, p-value = 3.971e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9415922 -0.7145938
sample estimates:
       cor 
-0.8678267 

52w VS 8w (SPF) < 0 and 104w VS 52w (SPF) < 0

[1] 12

	Pearson's product-moment correlation

data:  mat[, 1] and mat[, 2]
t = 4.3999, df = 10, p-value = 0.001335
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.4459551 0.9453668
sample estimates:
      cor 
0.8120303 

Summary

4
5
4
1